Intrusion detection is critical in ensuring computer system security. We present a unique approach for intrusion detection utilizing a zeroshot learning GAN (Generative Adversarial Network) in this paper. Our goal is to accurately recognize and classify incursions, especially those from unknown classes. To begin, we train the zero-shot learning GAN on a dataset that includes both normal and intrusive activity. The GAN is made up of a generator and a discriminator that are adversarial trained. The discriminator learns to discriminate between actual and produced data, while the generator learns to make synthetic data that mimics both visible and unseen classes. We reached a phenomenal accuracy of 99.9% on a test dataset consisting of instances from both shown and unseen classes after significant experimentation. This high level of precision highlights the efficiency of our zero-shot learning GAN technique in effectively identifying incursions, particularly those from previously unknown classes. Using zero-shot learning GANs, we describe a potential paradigm for intrusion detection. Our approach, which makes use of GANs and zero-shot learning, enables accurate intrusion classification even for classes that were not seen during training. The achieved 99.9% accuracy demonstrates the potential of our technique and its usefulness in improving computer system security.